ReviewKorean journal of radiology2023
Overcoming the Challenges in the Development and Implementation of Artificial Intelligence in Radiology: A Comprehensive Review of Solutions Beyond Supervised Learning.
Review in Korean journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Automated AI fracture detection in initial presentation pediatric wrist X-rays: effects and benefits of adding follow-up examinations.La Radiologia medica · 2026Article
- Lightweight deep learning framework for intracranial hemorrhage detection in brain CT scans.Frontiers in medicine · 2026Article
- Normal twin PET: personalized generative modeling for confounder correction and anomaly detection in whole-body PET/CT.Scientific reports · 2025Article
- Organ-based tumor distribution for predicting prognosis in small-cell lung cancer using fluorodeoxyglucose positron emission tomography/computed tomography.Scientific reports · 2025Article
- Enhancing Radiographic Diagnosis: CycleGAN-Based Methods for Reducing Cast Shadow Artifacts in Wrist Radiographs.Journal of imaging informatics in medicine · 2025Article
- Application of Artificial Intelligence in Thoracic Radiology: A Narrative Review.Tuberculosis and respiratory diseases · 2025Article
- Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
- Image-Based Generative Artificial Intelligence in Radiology: Comprehensive Updates.Korean journal of radiology · 2024Review
- Conceptual review of outcome metrics and measures used in clinical evaluation of artificial intelligence in radiology.La Radiologia medica · 2024Review
- [Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging].Journal of the Korean Society of Radiology · 2024Review
- 2023 Survey on User Experience of Artificial Intelligence Software in Radiology by the Korean Society of Radiology.Korean journal of radiology · 2024Article
- Enhancing diagnostic precision in liver lesion analysis using a deep learning-based system: opportunities and challenges.Nature reviews. Clinical oncology · 2024Article
- Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes.Korean journal of radiology · 2024Article
- [Visualization of Borderline Coronary Artery Lesions by CT Angiography and Coronary Artery Disease Reporting and Data System].Journal of the Korean Society of Radiology · 2024Review
- Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals.Korean journal of radiology · 2024Review
- The Promise of Explainable AI in Digital Health for Precision Medicine: A Systematic Review.Journal of personalized medicine · 2024Review
- Predictive Value of Simulated CT Radiomics Combined with Ipsilateral Lung Dosimetry Parameters for Radiation Pneumonitis in Patients with Esophageal Cancer: A Machine Learning-Based Retrospective Study.International journal of general medicine · 2024Article
- Artificial Intelligence in Breast Imaging: Opportunities, Challenges, and Legal-Ethical Considerations.The Eurasian journal of medicine · 2023Article
- Artificial intelligence in oncology: Current status and possibilities (Review).Medicine internationalReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.